Master of Science / Department of Computing and Information Sciences / William H. Hsu / The goal of my project is to summarize large volumes of data and help users to visualize how events have unfolded over time. I address the problem of extracting overview terms from a time-tagged corpus of data and discuss some previous work conducted in this area. I use a statistical approach to automatically extract key terms, form groupings of related terms, and display the resultant groups on a timeline. I use a static corpus composed of news stories, as opposed to an on-line setting where continual additions to the corpus are being made. Terms are extracted using a Named Entity Recognizer, and importance of a term is determined using the [superscript]X[superscript]2 measure. My approach does not address the problem of associating time and date stamps with data, and is restricted to corpora that been explicitly tagged. The quality of results obtained is gauged subjectively and objectively by measuring the degree to which events known to exist in the corpus were identified by the system.

This thesis investigates the problem of content selection for timeline generation from single history articles. While the task of timeline generation has been addressed before, most previous approaches assume the existence of a large corpus of history articles from the same era. They exploit the fact that salient information is likely to be mentioned multiple times in such corpora. However, large resources of this kind are only available for historical events that happened in the most recent decades. In this thesis, I present approaches which can be used to create history timelines for any historical period, even for eras such as the Middle Ages, for which no large corpora of supplementary text exist. The thesis first presents a system that selects relevant historical figures in a given article, a task which is substantially easier than full timeline generation. I show that a supervised approach which uses linguistic, structural and semantic features outperforms a competitive baseline on this task. Based on the observations made in this initial study, I then develop approaches for timeline generation. I find that an unsupervised approach that takes into account the article's subject area outperforms several supervised and unsupervised baselines. A main focus of this thesis is the development of evaluation methodologies and resources, as no suitable corpora existed when work began. For the initial experiment on important historical figures, I construct a corpus of existing timelines and textual articles, and devise a method for evaluating algorithms based on this resource. For timeline generation, I present a comprehensive evaluation methodology which is based on the interpretation of the task as a special form of single-document summarisation. This methodology scores algorithms based on meaning units rather than surface similarity. Unlike previous semantic-units-based evaluation methods for summarisation, my evaluation method does not require any manual annotation of system timelines. Once an evaluation resource has been created, which involves only annotation of the input texts, new timeline generation algorithms can be tested at no cost. This crucial advantage should make my new evaluation methodology attractive for the evaluation of general single-document summaries beyond timelines. I also present an evaluation resource which is based on this methodology. It was constructed using gold-standard timelines elicited from 30 human timeline writers, and has been made publicly available. This thesis concentrates on the content selection stage of timeline generation, and leaves the surface realisation step for future work. However, my evaluation methodology is designed in such a way that it can in principle also quantify the degree to which surface realisation is successful.